Evidence map›Paper›PMID 41083787›Full record

ArticleScientific reports2025

Single-cell RNA sequencing and Mendelian randomization, revealing molecular mechanisms and causal correlation of immune-related genes in periodontitis.

Xuedi Qiu, Fan Yang, Chenxi Li, Jian Wang, Yawen Yuan, Chao Guo

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xuedi Qiu *Department of Dentistry, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China.
Fan Yang *Department of Dentistry, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China.
Chenxi LiDepartment of Oral and Maxillofacial Oncology & Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Jian WangDepartment of Dentistry, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.
Yawen YuanDepartment of Dentistry, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China.
Chao GuoDepartment of Dentistry, First Affiliated Hospital of Shihezi University, Shihezi, 832000, China. guochao778@163.com.

Funding

Science Fund of Bingtuan,China 2024ZD072
6 · The paper itself

Abstract

The immune system has been linked to periodontitis risk in oral inflammation and systemic consequences. Specifically, this study investigated whether hub genes were associated with immune cells via integrating single-cell RNA sequencing (scRNA-seq) and Mendelian randomization (MR). We analyzed scRNA-seq data to identify differentially expressed genes (DEGs) and immune cell subtypes in periodontitis. First, MR analysis was conducted using eQTL data to determine causal relationships between immune cell gene expression and periodontitis risk. Then, Gene ontology (GO) and pathway enrichment analyses were performed to understand functional implications. In addition, CellChat trajectory analysis explored intercellular communication. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to validate hub genes. Comprehensive bioinformatics analysis delineated 23 immunologically distinct cell populations and identified 7 pivotal regulatory genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) demonstrating significant causal associations with periodontitis pathogenesis. Functional pathway analysis demonstrated these molecular markers participate in critical immunomodulatory pathways. RT-qPCR validation confirmed concordance between mRNA expression patterns of these DEGs and MR findings. Immunohistochemical analysis substantiated the causal relationships predicted by MR analysis, showing positive correlations for ANXA1 and SLC11A1 expression, while CD79B exhibited an inverse association. Advanced computational modeling of immune cell receptor-ligand interactions and cellular communication networks uncovered distinct functional roles during periodontitis development, providing novel insights into disease progression mechanisms. This study highlights the crucial role of immune cells and hub genes in periodontitis. These findings provide valuable insights into immune-related mechanisms and potential interventions for periodontitis.

Indexed as

Mendelian Randomization AnalysisPeriodontitisSingle-Cell AnalysisComputational BiologyGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansQuantitative Trait LociSequence Analysis, RNAImmune cellMachine learningMendelian randomizationPeriodontitisSingle-cell RNA sequencing

Identifiers

PMID41083787
PMCPMC12518570

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.